Genetic analysis of complex traits is one of the major challenges facing biomedical researchers in the so-called "'post-genomic era." Complex traits of particular interest include common diseases such as diabetes, asthma, hypertension, psychiatric illnesses, and cancer that together account for a large portion of the health care burden in the United States. They are familial, but do not have simple patterns of transmission and are likely to result from the actions and interactions of multiple genetic and environmental factors. The genetic architecture of such traits is complex and remains poorly characterized. Identifying and characterizing the genetic component to complex disorders should be useful in determining not only the primary defects for such disorders, but also in clarifying the role of environmental risk factors, which could also be targets of cost-effective treatment and prevention strategies. Study designs and methods of analysis that have worked well for simple Mendelian traits may not be sufficient for analysis of complex traits. The long-term objective of the proposed research is development of statistical methods for mapping and genetic analysis of human complex traits. The proposal is focused specifically on development of methods for linkage disequilibrium mapping of qualitative traits, with haplotype or multilocus genotype data. We propose to develop and test statistical models and methods and distribute software implementing new approaches in: (1) assessment of linkage disequilibrium in isolated founder populations with inbred pedigrees; 2) linkage disequilibrium mapping in isolated founder populations with inbred pedigrees; (3) assessment of high-resolution haplotype structure in outbred populations; and (4) methods for linkage disequilibrium mapping in outbred populations, that make use of information on high-resolution haplotype structure. Our preliminary studies indicate that these approaches can improve the ability to detect and localize genes for complex traits, thereby improving the odds for successful positional cloning.